In [1]:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
In [2]:
df = pd.read_csv('accident data.csv')
In [3]:
df.head()
Out[3]:
Index Accident_Severity Accident Date Latitude Light_Conditions District Area Longitude Number_of_Casualties Number_of_Vehicles Road_Surface_Conditions Road_Type Urban_or_Rural_Area Weather_Conditions Vehicle_Type
0 200701BS64157 Serious 05-06-2019 51.506187 Darkness - lights lit Kensington and Chelsea -0.209082 1 2 Dry Single carriageway Urban Fine no high winds Car
1 200701BS65737 Serious 02-07-2019 51.495029 Daylight Kensington and Chelsea -0.173647 1 2 Wet or damp Single carriageway Urban Raining no high winds Car
2 200701BS66127 Serious 26-08-2019 51.517715 Darkness - lighting unknown Kensington and Chelsea -0.210215 1 3 Dry NaN Urban NaN Taxi/Private hire car
3 200701BS66128 Serious 16-08-2019 51.495478 Daylight Kensington and Chelsea -0.202731 1 4 Dry Single carriageway Urban Fine no high winds Bus or coach (17 or more pass seats)
4 200701BS66837 Slight 03-09-2019 51.488576 Darkness - lights lit Kensington and Chelsea -0.192487 1 2 Dry NaN Urban NaN Other vehicle
In [4]:
df.tail()
Out[4]:
Index Accident_Severity Accident Date Latitude Light_Conditions District Area Longitude Number_of_Casualties Number_of_Vehicles Road_Surface_Conditions Road_Type Urban_or_Rural_Area Weather_Conditions Vehicle_Type
660674 201091NM01760 Slight 18-02-2022 57.374005 Daylight Highland -3.467828 2 1 Dry Single carriageway Rural Fine no high winds Car
660675 201091NM01881 Slight 21-02-2022 57.232273 Darkness - no lighting Highland -3.809281 1 1 Frost or ice Single carriageway Rural Fine no high winds Car
660676 201091NM01935 Slight 23-02-2022 57.585044 Daylight Highland -3.862727 1 3 Frost or ice Single carriageway Rural Fine no high winds Car
660677 201091NM01964 Serious 23-02-2022 57.214898 Darkness - no lighting Highland -3.823997 1 2 Wet or damp Single carriageway Rural Fine no high winds Motorcycle over 500cc
660678 201091NM02142 Serious 28-02-2022 57.575210 Daylight Highland -3.895673 1 1 Wet or damp Dual carriageway Rural Snowing no high winds Car
In [5]:
df.shape
Out[5]:
(660679, 14)
In [6]:
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 660679 entries, 0 to 660678
Data columns (total 14 columns):
 #   Column                   Non-Null Count   Dtype  
---  ------                   --------------   -----  
 0   Index                    660679 non-null  object 
 1   Accident_Severity        660679 non-null  object 
 2   Accident Date            660679 non-null  object 
 3   Latitude                 660654 non-null  float64
 4   Light_Conditions         660679 non-null  object 
 5   District Area            660679 non-null  object 
 6   Longitude                660653 non-null  float64
 7   Number_of_Casualties     660679 non-null  int64  
 8   Number_of_Vehicles       660679 non-null  int64  
 9   Road_Surface_Conditions  659953 non-null  object 
 10  Road_Type                656159 non-null  object 
 11  Urban_or_Rural_Area      660664 non-null  object 
 12  Weather_Conditions       646551 non-null  object 
 13  Vehicle_Type             660679 non-null  object 
dtypes: float64(2), int64(2), object(10)
memory usage: 70.6+ MB
In [7]:
df.columns
Out[7]:
Index(['Index', 'Accident_Severity', 'Accident Date', 'Latitude',
       'Light_Conditions', 'District Area', 'Longitude',
       'Number_of_Casualties', 'Number_of_Vehicles', 'Road_Surface_Conditions',
       'Road_Type', 'Urban_or_Rural_Area', 'Weather_Conditions',
       'Vehicle_Type'],
      dtype='object')
In [8]:
df.isnull().sum()
Out[8]:
Index                          0
Accident_Severity              0
Accident Date                  0
Latitude                      25
Light_Conditions               0
District Area                  0
Longitude                     26
Number_of_Casualties           0
Number_of_Vehicles             0
Road_Surface_Conditions      726
Road_Type                   4520
Urban_or_Rural_Area           15
Weather_Conditions         14128
Vehicle_Type                   0
dtype: int64
In [9]:
 df.duplicated().sum()
Out[9]:
19
In [10]:
df=df.drop_duplicates()
In [11]:
df.isnull().sum()
Out[11]:
Index                          0
Accident_Severity              0
Accident Date                  0
Latitude                      25
Light_Conditions               0
District Area                  0
Longitude                     26
Number_of_Casualties           0
Number_of_Vehicles             0
Road_Surface_Conditions      726
Road_Type                   4520
Urban_or_Rural_Area           15
Weather_Conditions         14127
Vehicle_Type                   0
dtype: int64
In [12]:
df.describe()
Out[12]:
Latitude Longitude Number_of_Casualties Number_of_Vehicles
count 660635.000000 660634.000000 660660.000000 660660.000000
mean 52.553857 -1.431216 1.357047 1.831255
std 1.406909 1.383322 0.824856 0.715272
min 49.914430 -7.516225 1.000000 1.000000
25% 51.490692 -2.332278 1.000000 1.000000
50% 52.315628 -1.411666 1.000000 2.000000
75% 53.453451 -0.232870 1.000000 2.000000
max 60.757544 1.762010 68.000000 32.000000
In [13]:
 df.nunique()
Out[13]:
Index                      421020
Accident_Severity               3
Accident Date                1461
Latitude                   511618
Light_Conditions                5
District Area                 422
Longitude                  529766
Number_of_Casualties           36
Number_of_Vehicles             19
Road_Surface_Conditions         5
Road_Type                       5
Urban_or_Rural_Area             3
Weather_Conditions              8
Vehicle_Type                   16
dtype: int64
In [14]:
 object_columns=df.select_dtypes(include=['object','bool']).columns
 print("Object type columns:")
 print(object_columns)
 numerical_columns=df.select_dtypes(include=['int64','float64']).columns
 print("\nNumerical type columns:")
 print(numerical_columns)

Object type columns:
Index(['Index', 'Accident_Severity', 'Accident Date', 'Light_Conditions',
       'District Area', 'Road_Surface_Conditions', 'Road_Type',
       'Urban_or_Rural_Area', 'Weather_Conditions', 'Vehicle_Type'],
      dtype='object')

Numerical type columns:
Index(['Latitude', 'Longitude', 'Number_of_Casualties', 'Number_of_Vehicles'], dtype='object')
In [15]:
def classify_features(df):
    categorical_features = []
    non_categorical_features = []
    discrete_features = []
    continuous_features = []
    for column in df.columns:
        if df[column].dtype in ['object', 'bool']:
            if df[column].nunique() < 15:
                categorical_features.append(column)
            else:
                non_categorical_features.append(column)
        elif df[column].dtype in ['int64', 'float64']:  
            if df[column].nunique() < 10:
                discrete_features.append(column)
            else:
                continuous_features.append(column)
    return categorical_features, non_categorical_features, discrete_features, continuous_features
In [16]:
categorical,non_categorical,discrete,continuous=classify_features(df)
In [17]:
 print("Categorical Features:",categorical)
 print("Non-Categorical Features:",non_categorical)
 print("Discrete Features:",discrete)
 print("Continuous Features:",continuous)

Categorical Features: ['Accident_Severity', 'Light_Conditions', 'Road_Surface_Conditions', 'Road_Type', 'Urban_or_Rural_Area', 'Weather_Conditions']
Non-Categorical Features: ['Index', 'Accident Date', 'District Area', 'Vehicle_Type']
Discrete Features: []
Continuous Features: ['Latitude', 'Longitude', 'Number_of_Casualties', 'Number_of_Vehicles']
In [18]:
null_counts = df.isnull().sum()
null_columns = null_counts[null_counts > 0].index.tolist()
In [19]:
total_rows = len(df)
null_percentage = (null_counts / total_rows) * 100
In [20]:
null_df = pd.DataFrame({
'Column': null_counts.index,
'Null Count': null_counts.values,
'Null Percentage': null_percentage.values
})
In [21]:
null_df = null_df.sort_values(by='Null Count', ascending=False)
In [22]:
null_df
Out[22]:
Column Null Count Null Percentage
12 Weather_Conditions 14127 2.138316
10 Road_Type 4520 0.684164
9 Road_Surface_Conditions 726 0.109890
6 Longitude 26 0.003935
3 Latitude 25 0.003784
11 Urban_or_Rural_Area 15 0.002270
0 Index 0 0.000000
1 Accident_Severity 0 0.000000
2 Accident Date 0 0.000000
4 Light_Conditions 0 0.000000
5 District Area 0 0.000000
7 Number_of_Casualties 0 0.000000
8 Number_of_Vehicles 0 0.000000
13 Vehicle_Type 0 0.000000
In [23]:
mode_value = df['Weather_Conditions'].mode()[0]
df['Weather_Conditions'].fillna(mode_value, inplace=True)
In [24]:
mode_value = df['Road_Type'].mode()[0]
df['Road_Type'].fillna(mode_value, inplace=True)
In [25]:
mode_value = df['Road_Surface_Conditions'].mode()[0]
df['Road_Surface_Conditions'].fillna(mode_value, inplace=True)
In [26]:
mode_value = df['Urban_or_Rural_Area'].mode()[0]
df['Urban_or_Rural_Area'].fillna(mode_value, inplace=True)
In [27]:
mean_value = df['Latitude'].mean()
df['Latitude'].fillna(mean_value, inplace=True)
In [28]:
mean_value = df['Longitude'].mean()
df['Longitude'].fillna(mean_value, inplace=True)
In [29]:
df.isnull().sum()
Out[29]:
Index                      0
Accident_Severity          0
Accident Date              0
Latitude                   0
Light_Conditions           0
District Area              0
Longitude                  0
Number_of_Casualties       0
Number_of_Vehicles         0
Road_Surface_Conditions    0
Road_Type                  0
Urban_or_Rural_Area        0
Weather_Conditions         0
Vehicle_Type               0
dtype: int64
In [30]:
for i in categorical:
    print(i, ':')
    print(df[i].unique())
    print()

Accident_Severity :
['Serious' 'Slight' 'Fatal']

Light_Conditions :
['Darkness - lights lit' 'Daylight' 'Darkness - lighting unknown'
 'Darkness - lights unlit' 'Darkness - no lighting']

Road_Surface_Conditions :
['Dry' 'Wet or damp' 'Snow' 'Frost or ice' 'Flood over 3cm. deep']

Road_Type :
['Single carriageway' 'Dual carriageway' 'One way street' 'Roundabout'
 'Slip road']

Urban_or_Rural_Area :
['Urban' 'Rural' 'Unallocated']

Weather_Conditions :
['Fine no high winds' 'Raining no high winds' 'Other' 'Fine + high winds'
 'Raining + high winds' 'Snowing no high winds' 'Fog or mist'
 'Snowing + high winds']
In [31]:
for i in categorical:
    print(i, ':')
    print(df[i].value_counts())
    print()

Accident_Severity :
Slight     563782
Serious     88217
Fatal        8661
Name: Accident_Severity, dtype: int64

Light_Conditions :
Daylight                       484866
Darkness - lights lit          129335
Darkness - no lighting          37432
Darkness - lighting unknown      6484
Darkness - lights unlit          2543
Name: Light_Conditions, dtype: int64

Road_Surface_Conditions :
Dry                     448536
Wet or damp             186705
Frost or ice             18514
Snow                      5888
Flood over 3cm. deep      1017
Name: Road_Surface_Conditions, dtype: int64

Road_Type :
Single carriageway    496655
Dual carriageway       99416
Roundabout             43989
One way street         13559
Slip road               7041
Name: Road_Type, dtype: int64

Urban_or_Rural_Area :
Urban          421675
Rural          238974
Unallocated        11
Name: Urban_or_Rural_Area, dtype: int64

Weather_Conditions :
Fine no high winds       534999
Raining no high winds     79696
Other                     17148
Raining + high winds       9615
Fine + high winds          8553
Snowing no high winds      6237
Fog or mist                3528
Snowing + high winds        884
Name: Weather_Conditions, dtype: int64
In [32]:
for i in categorical:
    plt.figure(figsize=(15, 6))
    ax = sns.countplot(x=i, data=df, palette='hls')
    for p in ax.patches:
        ax.annotate(f'{p.get_height()}', (p.get_x() + p.get_width() / 2., p.get_height()),
                    ha='center', va='center', xytext=(0, 10), textcoords='offset points')
    plt.show()
In [33]:
for i in categorical:
    plt.figure(figsize=(20,10))
    plt.pie(df[i].value_counts(),labels=df[i].value_counts().index,autopct='%1.1f%%',textprops={'fontsize':15,
       'color':'black',
       'weight':'bold',
       'family':'serif'})
    hfont = {'fontname':'serif','weight':'bold'}
    plt.title(i,size=20,**hfont)
    plt.show()
In [34]:
for i in continuous:
    plt.figure(figsize=(15,6))
    sns.histplot(df[i], bins = 20,kde = True,palette='hls')
    plt.show()

C:\Users\tamil\AppData\Local\Temp\ipykernel_3900\2283040530.py:3: UserWarning: Ignoring `palette` because no `hue` variable has been assigned.
  sns.histplot(df[i], bins = 20,kde = True,palette='hls')

C:\Users\tamil\AppData\Local\Temp\ipykernel_3900\2283040530.py:3: UserWarning: Ignoring `palette` because no `hue` variable has been assigned.
  sns.histplot(df[i], bins = 20,kde = True,palette='hls')

C:\Users\tamil\AppData\Local\Temp\ipykernel_3900\2283040530.py:3: UserWarning: Ignoring `palette` because no `hue` variable has been assigned.
  sns.histplot(df[i], bins = 20,kde = True,palette='hls')

C:\Users\tamil\AppData\Local\Temp\ipykernel_3900\2283040530.py:3: UserWarning: Ignoring `palette` because no `hue` variable has been assigned.
  sns.histplot(df[i], bins = 20,kde = True,palette='hls')
In [35]:
for i in continuous:
    plt.figure(figsize=(15,6))
    sns.distplot(df[i], bins = 20, kde = True)
    plt.show()

C:\Users\tamil\AppData\Local\Temp\ipykernel_3900\3589386306.py:3: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(df[i], bins = 20, kde = True)

C:\Users\tamil\AppData\Local\Temp\ipykernel_3900\3589386306.py:3: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(df[i], bins = 20, kde = True)

C:\Users\tamil\AppData\Local\Temp\ipykernel_3900\3589386306.py:3: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(df[i], bins = 20, kde = True)

C:\Users\tamil\AppData\Local\Temp\ipykernel_3900\3589386306.py:3: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(df[i], bins = 20, kde = True)
In [36]:
import warnings
warnings.filterwarnings('ignore')
In [37]:
for i in continuous:
    plt.figure(figsize=(15, 6))
    sns.boxplot(x=i, data=df, palette='hls')  # Pass `i` as `x=i`
    plt.show()
In [38]:
for i in continuous:
    plt.figure(figsize=(15,6))
    sns.boxenplot(x=i, data = df, palette='hls')
    plt.show()
In [39]:
for i in continuous:
    plt.figure(figsize=(15,6))
    sns.violinplot(x = i, data = df, palette='hls')
    plt.show()
In [40]:
for i in continuous:
    for j in continuous:
        if i != j:
            plt.figure(figsize=(15, 6))
            sns.scatterplot(x=i, y=j, data=df, palette='hls')
            plt.xticks(rotation=90)
            plt.show()
In [41]:
for i in categorical:
    for j in continuous:
        plt.figure(figsize=(15,6))
        sns.barplot(x = df[i], y = df[j], data = df,palette = 'hls')
        plt.xticks(rotation = 90)
        plt.show()
In [42]:
for i in categorical:
    for j in continuous:
        plt.figure(figsize=(15,6))
        sns.boxplot(x = df[i], y = df[j], data = df, palette = 'hls')
        plt.xticks(rotation = 90)
        plt.show()
In [43]:
for i in categorical:
    for j in continuous:
        plt.figure(figsize=(15,6))
        sns.boxenplot(x = df[i], y = df[j], data = df, palette = 'hls')
        plt.xticks(rotation = 90)
        plt.show()
In [44]:
for i in categorical:
    for j in continuous:
        plt.figure(figsize=(15,6))
        sns.violinplot(x = df[i], y = df[j], data = df, palette = 'hls')
        plt.xticks(rotation = 90)
        plt.show()
In [32]:
correlation_matrix = df.corr()

C:\Users\tamil\AppData\Local\Temp\ipykernel_28612\4214245630.py:1: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.
  correlation_matrix = df.corr()
In [33]:
correlation_matrix
Out[33]:
Latitude Longitude Number_of_Casualties Number_of_Vehicles
Latitude 1.000000 -0.398116 0.032198 -0.040028
Longitude -0.398116 1.000000 -0.040404 0.014716
Number_of_Casualties 0.032198 -0.040404 1.000000 0.228889
Number_of_Vehicles -0.040028 0.014716 0.228889 1.000000
In [34]:
plt.figure(figsize=(15, 6))
sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt=".2f")
plt.show()
In [35]:
df_new = df.copy()
In [36]:
df_new
Out[36]:
Index Accident_Severity Accident Date Latitude Light_Conditions District Area Longitude Number_of_Casualties Number_of_Vehicles Road_Surface_Conditions Road_Type Urban_or_Rural_Area Weather_Conditions Vehicle_Type
0 200701BS64157 Serious 05-06-2019 51.506187 Darkness - lights lit Kensington and Chelsea -0.209082 1 2 Dry Single carriageway Urban Fine no high winds Car
1 200701BS65737 Serious 02-07-2019 51.495029 Daylight Kensington and Chelsea -0.173647 1 2 Wet or damp Single carriageway Urban Raining no high winds Car
2 200701BS66127 Serious 26-08-2019 51.517715 Darkness - lighting unknown Kensington and Chelsea -0.210215 1 3 Dry Single carriageway Urban Fine no high winds Taxi/Private hire car
3 200701BS66128 Serious 16-08-2019 51.495478 Daylight Kensington and Chelsea -0.202731 1 4 Dry Single carriageway Urban Fine no high winds Bus or coach (17 or more pass seats)
4 200701BS66837 Slight 03-09-2019 51.488576 Darkness - lights lit Kensington and Chelsea -0.192487 1 2 Dry Single carriageway Urban Fine no high winds Other vehicle
... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
660674 201091NM01760 Slight 18-02-2022 57.374005 Daylight Highland -3.467828 2 1 Dry Single carriageway Rural Fine no high winds Car
660675 201091NM01881 Slight 21-02-2022 57.232273 Darkness - no lighting Highland -3.809281 1 1 Frost or ice Single carriageway Rural Fine no high winds Car
660676 201091NM01935 Slight 23-02-2022 57.585044 Daylight Highland -3.862727 1 3 Frost or ice Single carriageway Rural Fine no high winds Car
660677 201091NM01964 Serious 23-02-2022 57.214898 Darkness - no lighting Highland -3.823997 1 2 Wet or damp Single carriageway Rural Fine no high winds Motorcycle over 500cc
660678 201091NM02142 Serious 28-02-2022 57.575210 Daylight Highland -3.895673 1 1 Wet or damp Dual carriageway Rural Snowing no high winds Car

660660 rows × 14 columns

In [37]:
df_new['Accident Date'] = pd.to_datetime(df_new['Accident Date'])

C:\Users\tamil\AppData\Local\Temp\ipykernel_28612\1543151786.py:1: UserWarning: Parsing dates in DD/MM/YYYY format when dayfirst=False (the default) was specified. This may lead to inconsistently parsed dates! Specify a format to ensure consistent parsing.
  df_new['Accident Date'] = pd.to_datetime(df_new['Accident Date'])
In [38]:
df_new.set_index('Accident Date', inplace=True)
In [39]:
df_new
Out[39]:
Index Accident_Severity Latitude Light_Conditions District Area Longitude Number_of_Casualties Number_of_Vehicles Road_Surface_Conditions Road_Type Urban_or_Rural_Area Weather_Conditions Vehicle_Type
Accident Date
2019-05-06 200701BS64157 Serious 51.506187 Darkness - lights lit Kensington and Chelsea -0.209082 1 2 Dry Single carriageway Urban Fine no high winds Car
2019-02-07 200701BS65737 Serious 51.495029 Daylight Kensington and Chelsea -0.173647 1 2 Wet or damp Single carriageway Urban Raining no high winds Car
2019-08-26 200701BS66127 Serious 51.517715 Darkness - lighting unknown Kensington and Chelsea -0.210215 1 3 Dry Single carriageway Urban Fine no high winds Taxi/Private hire car
2019-08-16 200701BS66128 Serious 51.495478 Daylight Kensington and Chelsea -0.202731 1 4 Dry Single carriageway Urban Fine no high winds Bus or coach (17 or more pass seats)
2019-03-09 200701BS66837 Slight 51.488576 Darkness - lights lit Kensington and Chelsea -0.192487 1 2 Dry Single carriageway Urban Fine no high winds Other vehicle
... ... ... ... ... ... ... ... ... ... ... ... ... ...
2022-02-18 201091NM01760 Slight 57.374005 Daylight Highland -3.467828 2 1 Dry Single carriageway Rural Fine no high winds Car
2022-02-21 201091NM01881 Slight 57.232273 Darkness - no lighting Highland -3.809281 1 1 Frost or ice Single carriageway Rural Fine no high winds Car
2022-02-23 201091NM01935 Slight 57.585044 Daylight Highland -3.862727 1 3 Frost or ice Single carriageway Rural Fine no high winds Car
2022-02-23 201091NM01964 Serious 57.214898 Darkness - no lighting Highland -3.823997 1 2 Wet or damp Single carriageway Rural Fine no high winds Motorcycle over 500cc
2022-02-28 201091NM02142 Serious 57.575210 Daylight Highland -3.895673 1 1 Wet or damp Dual carriageway Rural Snowing no high winds Car

660660 rows × 13 columns

In [40]:
plt.figure(figsize=(14, 8))
df_new.resample('M').size().plot(legend=False)
plt.title('Number of Accidents Over Time - Monthly')
plt.xlabel('Date')
plt.ylabel('Number of Accidents')
plt.show()
In [41]:
df['Accident Date'] = pd.to_datetime(df['Accident Date'])
df_new = df.copy()
df_new.set_index('Accident Date', inplace=True)

# Now plot
plt.figure(figsize=(14, 8))
df_new.resample('M').size().plot(legend=False)
plt.title('Number of Accidents Over Time - Monthly')
plt.xlabel('Date')
plt.ylabel('Number of Accidents')
plt.show()

C:\Users\tamil\AppData\Local\Temp\ipykernel_28612\950211844.py:1: UserWarning: Parsing dates in DD/MM/YYYY format when dayfirst=False (the default) was specified. This may lead to inconsistently parsed dates! Specify a format to ensure consistent parsing.
  df['Accident Date'] = pd.to_datetime(df['Accident Date'])
In [42]:
plt.figure(figsize=(14, 8))
df_new.resample('D').size().plot(legend=False)
plt.title('Number of Accidents Over Time - Daily')
plt.xlabel('Date')
plt.ylabel('Number of Accidents')
plt.show()
In [43]:
plt.figure(figsize=(14, 8))
df_new.resample('Y').size().plot(legend=False)
plt.title('Number of Accidents Over Time - Yearly')
plt.xlabel('Date')
plt.ylabel('Number of Accidents')
plt.show()
In [44]:
pivot_table_result = pd.pivot_table(df, values='Number_of_Casualties', index='Accident Date', columns='Accident_Severity',aggfunc='sum', fill_value=0)
pivot_table_result
Out[44]:
Accident_Severity Fatal Serious Slight
Accident Date
2019-01-01 19 74 401
2019-01-02 9 134 774
2019-01-03 17 89 557
2019-01-04 16 107 437
2019-01-05 20 93 618
... ... ... ...
2022-12-27 8 34 224
2022-12-28 12 39 223
2022-12-29 2 46 255
2022-12-30 0 33 209
2022-12-31 8 43 211

1461 rows × 3 columns

In [45]:
pivot_table_result = pd.pivot_table(df, values='Number_of_Casualties', index='Accident Date', columns='Accident_Severity',aggfunc='sum', fill_value=0)
pivot_table_result
Out[45]:
Accident_Severity Fatal Serious Slight
Accident Date
2019-01-01 19 74 401
2019-01-02 9 134 774
2019-01-03 17 89 557
2019-01-04 16 107 437
2019-01-05 20 93 618
... ... ... ...
2022-12-27 8 34 224
2022-12-28 12 39 223
2022-12-29 2 46 255
2022-12-30 0 33 209
2022-12-31 8 43 211

1461 rows × 3 columns

In [46]:
pivot_table_result = pd.pivot_table(df, values='Number_of_Vehicles', index='Accident Date', columns='Accident_Severity',aggfunc='count',fill_value=0)
pivot_table_result
Out[46]:
Accident_Severity Fatal Serious Slight
Accident Date
2019-01-01 11 53 271
2019-01-02 7 79 590
2019-01-03 11 64 439
2019-01-04 10 70 306
2019-01-05 9 67 500
... ... ... ...
2022-12-27 3 18 158
2022-12-28 6 27 148
2022-12-29 1 25 188
2022-12-30 0 21 150
2022-12-31 4 24 165

1461 rows × 3 columns

In [47]:
pivot_table_result = pd.pivot_table(df, values='Number_of_Vehicles', index='Accident Date', columns='Accident_Severity',aggfunc='sum', fill_value=0)
pivot_table_result
Out[47]:
Accident_Severity Fatal Serious Slight
Accident Date
2019-01-01 12 76 463
2019-01-02 10 142 1076
2019-01-03 15 106 801
2019-01-04 14 122 557
2019-01-05 17 125 968
... ... ... ...
2022-12-27 6 34 268
2022-12-28 9 39 257
2022-12-29 2 44 341
2022-12-30 0 32 270
2022-12-31 5 38 293

1461 rows × 3 columns

In [48]:
crosstab_result = pd.crosstab(index=df['Accident_Severity'], columns=df['Weather_Conditions'])
crosstab_result
Out[48]:
Weather_Conditions Fine + high winds Fine no high winds Fog or mist Other Raining + high winds Raining no high winds Snowing + high winds Snowing no high winds
Accident_Severity
Fatal 175 7207 82 165 145 848 3 36
Serious 1245 73285 483 1801 1261 9468 109 565
Slight 7133 454507 2963 15182 8209 69380 772 5636
In [49]:
crosstab_result = pd.crosstab(index=df['Accident_Severity'], columns=df['Light_Conditions'])
crosstab_result
Out[49]:
Light_Conditions Darkness - lighting unknown Darkness - lights lit Darkness - lights unlit Darkness - no lighting Daylight
Accident_Severity
Fatal 68 1860 45 1612 5076
Serious 794 19130 360 7174 60759
Slight 5622 108345 2138 28646 419031
In [50]:
crosstab_result = pd.crosstab(index=df['Accident_Severity'], columns=df['Road_Surface_Conditions'])
crosstab_result
Out[50]:
Road_Surface_Conditions Dry Flood over 3cm. deep Frost or ice Snow Wet or damp
Accident_Severity
Fatal 5790 23 193 35 2620
Serious 61708 152 2007 565 23785
Slight 381038 842 16314 5288 160300
In [51]:
crosstab_result = pd.crosstab(index=df['Accident_Severity'], columns=df['Road_Type'])
crosstab_result
Out[51]:
Road_Type Dual carriageway One way street Roundabout Single carriageway Slip road
Accident_Severity
Fatal 1815 95 142 6560 49
Serious 11746 1655 3665 70540 611
Slight 85855 11809 40182 419555 6381
In [52]:
crosstab_result = pd.crosstab(index=df['Accident_Severity'], columns=df['Urban_or_Rural_Area'])
crosstab_result
Out[52]:
Urban_or_Rural_Area Rural Unallocated Urban
Accident_Severity
Fatal 5601 0 3060
Serious 37312 1 50904
Slight 196061 10 367711
In [53]:
groupby_result = df.groupby(['Accident_Severity', 'Weather_Conditions'])['Number_of_Casualties'].max().unstack()
groupby_result
Out[53]:
Weather_Conditions Fine + high winds Fine no high winds Fog or mist Other Raining + high winds Raining no high winds Snowing + high winds Snowing no high winds
Accident_Severity
Fatal 9 62 42 48 8 68 3 7
Serious 10 45 11 18 9 21 6 7
Slight 19 43 14 9 12 47 8 7
In [54]:
groupby_result = df.groupby(['Accident_Severity', 'Weather_Conditions'])['Number_of_Casualties'].min().unstack()
groupby_result
Out[54]:
Weather_Conditions Fine + high winds Fine no high winds Fog or mist Other Raining + high winds Raining no high winds Snowing + high winds Snowing no high winds
Accident_Severity
Fatal 1 1 1 1 1 1 2 1
Serious 1 1 1 1 1 1 1 1
Slight 1 1 1 1 1 1 1 1
In [55]:
groupby_result = df.groupby(['Accident_Severity', 'Light_Conditions'])['Number_of_Casualties'].max().unstack()
groupby_result
Out[55]:
Light_Conditions Darkness - lighting unknown Darkness - lights lit Darkness - lights unlit Darkness - no lighting Daylight
Accident_Severity
Fatal 6 68 6 48 62
Serious 8 15 7 21 45
Slight 8 27 8 13 47
In [56]:
groupby_result = df.groupby(['Accident_Severity', 'Light_Conditions'])['Number_of_Casualties'].min().unstack()
groupby_result
Out[56]:
Light_Conditions Darkness - lighting unknown Darkness - lights lit Darkness - lights unlit Darkness - no lighting Daylight
Accident_Severity
Fatal 1 1 1 1 1
Serious 1 1 1 1 1
Slight 1 1 1 1 1
In [57]:
groupby_result = df.groupby(['Accident_Severity', 'Road_Surface_Conditions'])['Number_of_Casualties'].max().unstack()
groupby_result
Out[57]:
Road_Surface_Conditions Dry Flood over 3cm. deep Frost or ice Snow Wet or damp
Accident_Severity
Fatal 40 7 48 7 68
Serious 45 7 13 18 21
Slight 43 9 15 7 47
In [58]:
groupby_result = df.groupby(['Accident_Severity', 'Road_Surface_Conditions'])['Number_of_Casualties'].min().unstack()
groupby_result
Out[58]:
Road_Surface_Conditions Dry Flood over 3cm. deep Frost or ice Snow Wet or damp
Accident_Severity
Fatal 1 1 1 1 1
Serious 1 1 1 1 1
Slight 1 1 1 1 1
In [59]:
groupby_result = df.groupby(['Accident_Severity', 'Road_Type'])['Number_of_Casualties'].max().unstack()
groupby_result
Out[59]:
Road_Type Dual carriageway One way street Roundabout Single carriageway Slip road
Accident_Severity
Fatal 42 8 4 62 68
Serious 45 15 19 42 16
Slight 47 13 17 29 14
In [60]:
groupby_result = df.groupby(['Accident_Severity', 'Road_Type'])['Number_of_Casualties'].min().unstack()
groupby_result
Out[60]:
Road_Type Dual carriageway One way street Roundabout Single carriageway Slip road
Accident_Severity
Fatal 1 1 1 1 1
Serious 1 1 1 1 1
Slight 1 1 1 1 1
In [61]:
groupby_result = df.groupby(['Accident_Severity', 'Urban_or_Rural_Area'])['Number_of_Casualties'].max().unstack()
groupby_result
Out[61]:
Urban_or_Rural_Area Rural Unallocated Urban
Accident_Severity
Fatal 68.0 NaN 21.0
Serious 45.0 1.0 29.0
Slight 47.0 3.0 32.0
In [62]:
groupby_result = df.groupby(['Accident_Severity', 'Urban_or_Rural_Area'])['Number_of_Casualties'].min().unstack()
groupby_result
Out[62]:
Urban_or_Rural_Area Rural Unallocated Urban
Accident_Severity
Fatal 1.0 NaN 1.0
Serious 1.0 1.0 1.0
Slight 1.0 1.0 1.0
In [63]:
df
Out[63]:
Index Accident_Severity Accident Date Latitude Light_Conditions District Area Longitude Number_of_Casualties Number_of_Vehicles Road_Surface_Conditions Road_Type Urban_or_Rural_Area Weather_Conditions Vehicle_Type
0 200701BS64157 Serious 2019-05-06 51.506187 Darkness - lights lit Kensington and Chelsea -0.209082 1 2 Dry Single carriageway Urban Fine no high winds Car
1 200701BS65737 Serious 2019-02-07 51.495029 Daylight Kensington and Chelsea -0.173647 1 2 Wet or damp Single carriageway Urban Raining no high winds Car
2 200701BS66127 Serious 2019-08-26 51.517715 Darkness - lighting unknown Kensington and Chelsea -0.210215 1 3 Dry Single carriageway Urban Fine no high winds Taxi/Private hire car
3 200701BS66128 Serious 2019-08-16 51.495478 Daylight Kensington and Chelsea -0.202731 1 4 Dry Single carriageway Urban Fine no high winds Bus or coach (17 or more pass seats)
4 200701BS66837 Slight 2019-03-09 51.488576 Darkness - lights lit Kensington and Chelsea -0.192487 1 2 Dry Single carriageway Urban Fine no high winds Other vehicle
... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
660674 201091NM01760 Slight 2022-02-18 57.374005 Daylight Highland -3.467828 2 1 Dry Single carriageway Rural Fine no high winds Car
660675 201091NM01881 Slight 2022-02-21 57.232273 Darkness - no lighting Highland -3.809281 1 1 Frost or ice Single carriageway Rural Fine no high winds Car
660676 201091NM01935 Slight 2022-02-23 57.585044 Daylight Highland -3.862727 1 3 Frost or ice Single carriageway Rural Fine no high winds Car
660677 201091NM01964 Serious 2022-02-23 57.214898 Darkness - no lighting Highland -3.823997 1 2 Wet or damp Single carriageway Rural Fine no high winds Motorcycle over 500cc
660678 201091NM02142 Serious 2022-02-28 57.575210 Daylight Highland -3.895673 1 1 Wet or damp Dual carriageway Rural Snowing no high winds Car

660660 rows × 14 columns

In [67]:
df = df.drop(['Index','Accident Date','District Area'],axis=1)

---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
Cell In[67], line 1
----> 1 df = df.drop(['Index','Accident Date','District Area'],axis=1)

File D:\Users\tamil\anaconda3\Lib\site-packages\pandas\util\_decorators.py:331, in deprecate_nonkeyword_arguments.<locals>.decorate.<locals>.wrapper(*args, **kwargs)
    325 if len(args) > num_allow_args:
    326     warnings.warn(
    327         msg.format(arguments=_format_argument_list(allow_args)),
    328         FutureWarning,
    329         stacklevel=find_stack_level(),
    330     )
--> 331 return func(*args, **kwargs)

File D:\Users\tamil\anaconda3\Lib\site-packages\pandas\core\frame.py:5399, in DataFrame.drop(self, labels, axis, index, columns, level, inplace, errors)
   5251 @deprecate_nonkeyword_arguments(version=None, allowed_args=["self", "labels"])
   5252 def drop(  # type: ignore[override]
   5253     self,
   (...)
   5260     errors: IgnoreRaise = "raise",
   5261 ) -> DataFrame | None:
   5262     """
   5263     Drop specified labels from rows or columns.
   5264 
   (...)
   5397             weight  1.0     0.8
   5398     """
-> 5399     return super().drop(
   5400         labels=labels,
   5401         axis=axis,
   5402         index=index,
   5403         columns=columns,
   5404         level=level,
   5405         inplace=inplace,
   5406         errors=errors,
   5407     )

File D:\Users\tamil\anaconda3\Lib\site-packages\pandas\util\_decorators.py:331, in deprecate_nonkeyword_arguments.<locals>.decorate.<locals>.wrapper(*args, **kwargs)
    325 if len(args) > num_allow_args:
    326     warnings.warn(
    327         msg.format(arguments=_format_argument_list(allow_args)),
    328         FutureWarning,
    329         stacklevel=find_stack_level(),
    330     )
--> 331 return func(*args, **kwargs)

File D:\Users\tamil\anaconda3\Lib\site-packages\pandas\core\generic.py:4505, in NDFrame.drop(self, labels, axis, index, columns, level, inplace, errors)
   4503 for axis, labels in axes.items():
   4504     if labels is not None:
-> 4505         obj = obj._drop_axis(labels, axis, level=level, errors=errors)
   4507 if inplace:
   4508     self._update_inplace(obj)

File D:\Users\tamil\anaconda3\Lib\site-packages\pandas\core\generic.py:4546, in NDFrame._drop_axis(self, labels, axis, level, errors, only_slice)
   4544         new_axis = axis.drop(labels, level=level, errors=errors)
   4545     else:
-> 4546         new_axis = axis.drop(labels, errors=errors)
   4547     indexer = axis.get_indexer(new_axis)
   4549 # Case for non-unique axis
   4550 else:

File D:\Users\tamil\anaconda3\Lib\site-packages\pandas\core\indexes\base.py:6934, in Index.drop(self, labels, errors)
   6932 if mask.any():
   6933     if errors != "ignore":
-> 6934         raise KeyError(f"{list(labels[mask])} not found in axis")
   6935     indexer = indexer[~mask]
   6936 return self.delete(indexer)

KeyError: "['Index', 'Accident Date', 'District Area'] not found in axis"
In [68]:
df = df.drop(['Index', 'Accident Date', 'District Area'], axis=1, errors='ignore')
In [77]:
print(df.columns)

Index(['Accident_Severity', 'Number_of_Casualties', 'Number_of_Vehicles',
       'Light_Conditions_Darkness - lighting unknown',
       'Light_Conditions_Darkness - lights lit',
       'Light_Conditions_Darkness - lights unlit',
       'Light_Conditions_Darkness - no lighting', 'Light_Conditions_Daylight',
       'Road_Surface_Conditions_Dry',
       'Road_Surface_Conditions_Flood over 3cm. deep',
       'Road_Surface_Conditions_Frost or ice', 'Road_Surface_Conditions_Snow',
       'Road_Surface_Conditions_Wet or damp', 'Road_Type_Dual carriageway',
       'Road_Type_One way street', 'Road_Type_Roundabout',
       'Road_Type_Single carriageway', 'Road_Type_Slip road',
       'Urban_or_Rural_Area_Rural', 'Urban_or_Rural_Area_Unallocated',
       'Urban_or_Rural_Area_Urban', 'Vehicle_Type_Agricultural vehicle',
       'Vehicle_Type_Bus or coach (17 or more pass seats)', 'Vehicle_Type_Car',
       'Vehicle_Type_Data missing or out of range',
       'Vehicle_Type_Goods 7.5 tonnes mgw and over',
       'Vehicle_Type_Goods over 3.5t. and under 7.5t',
       'Vehicle_Type_Minibus (8 - 16 passenger seats)',
       'Vehicle_Type_Motorcycle 125cc and under',
       'Vehicle_Type_Motorcycle 50cc and under',
       'Vehicle_Type_Motorcycle over 125cc and up to 500cc',
       'Vehicle_Type_Motorcycle over 500cc', 'Vehicle_Type_Other vehicle',
       'Vehicle_Type_Pedal cycle', 'Vehicle_Type_Ridden horse',
       'Vehicle_Type_Taxi/Private hire car',
       'Vehicle_Type_Van / Goods 3.5 tonnes mgw or under',
       'Weather_Conditions_Fine + high winds',
       'Weather_Conditions_Fine no high winds',
       'Weather_Conditions_Fog or mist', 'Weather_Conditions_Other',
       'Weather_Conditions_Raining + high winds',
       'Weather_Conditions_Raining no high winds',
       'Weather_Conditions_Snowing + high winds',
       'Weather_Conditions_Snowing no high winds'],
      dtype='object')
In [78]:
label_mapping = {'Slight': 0, 'Serious': 1, 'Fatal': 2}
df['Accident_Severity'] = df['Accident_Severity'].map(label_mapping)
In [79]:
df = pd.get_dummies(df,columns = ['Light_Conditions','Road_Surface_Conditions','Road_Type','Urban_or_Rural_Area','Vehicle_Type','Weather_Conditions'])

---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
Cell In[79], line 1
----> 1 df = pd.get_dummies(df,columns = ['Light_Conditions','Road_Surface_Conditions','Road_Type','Urban_or_Rural_Area','Vehicle_Type','Weather_Conditions'])

File D:\Users\tamil\anaconda3\Lib\site-packages\pandas\core\reshape\encoding.py:146, in get_dummies(data, prefix, prefix_sep, dummy_na, columns, sparse, drop_first, dtype)
    144     raise TypeError("Input must be a list-like for parameter `columns`")
    145 else:
--> 146     data_to_encode = data[columns]
    148 # validate prefixes and separator to avoid silently dropping cols
    149 def check_len(item, name):

File D:\Users\tamil\anaconda3\Lib\site-packages\pandas\core\frame.py:3813, in DataFrame.__getitem__(self, key)
   3811     if is_iterator(key):
   3812         key = list(key)
-> 3813     indexer = self.columns._get_indexer_strict(key, "columns")[1]
   3815 # take() does not accept boolean indexers
   3816 if getattr(indexer, "dtype", None) == bool:

File D:\Users\tamil\anaconda3\Lib\site-packages\pandas\core\indexes\base.py:6070, in Index._get_indexer_strict(self, key, axis_name)
   6067 else:
   6068     keyarr, indexer, new_indexer = self._reindex_non_unique(keyarr)
-> 6070 self._raise_if_missing(keyarr, indexer, axis_name)
   6072 keyarr = self.take(indexer)
   6073 if isinstance(key, Index):
   6074     # GH 42790 - Preserve name from an Index

File D:\Users\tamil\anaconda3\Lib\site-packages\pandas\core\indexes\base.py:6130, in Index._raise_if_missing(self, key, indexer, axis_name)
   6128     if use_interval_msg:
   6129         key = list(key)
-> 6130     raise KeyError(f"None of [{key}] are in the [{axis_name}]")
   6132 not_found = list(ensure_index(key)[missing_mask.nonzero()[0]].unique())
   6133 raise KeyError(f"{not_found} not in index")

KeyError: "None of [Index(['Light_Conditions', 'Road_Surface_Conditions', 'Road_Type',\n       'Urban_or_Rural_Area', 'Vehicle_Type', 'Weather_Conditions'],\n      dtype='object')] are in the [columns]"
In [75]:
# Check if the columns are present
print(df.columns)

# Strip extra whitespaces if any
df.columns = df.columns.str.strip()

# Check for missing columns
required_columns = ['Light_Conditions', 'Road_Surface_Conditions', 'Road_Type', 
                    'Urban_or_Rural_Area', 'Vehicle_Type', 'Weather_Conditions']

missing_columns = [col for col in required_columns if col not in df.columns]

if missing_columns:
    print(f"Missing columns: {missing_columns}")
else:
    # Apply pd.get_dummies if all columns are present
    df = pd.get_dummies(df, columns=required_columns)

# Print the updated DataFrame columns after applying get_dummies
print(df.columns)

Index(['Accident_Severity', 'Number_of_Casualties', 'Number_of_Vehicles',
       'Light_Conditions_Darkness - lighting unknown',
       'Light_Conditions_Darkness - lights lit',
       'Light_Conditions_Darkness - lights unlit',
       'Light_Conditions_Darkness - no lighting', 'Light_Conditions_Daylight',
       'Road_Surface_Conditions_Dry',
       'Road_Surface_Conditions_Flood over 3cm. deep',
       'Road_Surface_Conditions_Frost or ice', 'Road_Surface_Conditions_Snow',
       'Road_Surface_Conditions_Wet or damp', 'Road_Type_Dual carriageway',
       'Road_Type_One way street', 'Road_Type_Roundabout',
       'Road_Type_Single carriageway', 'Road_Type_Slip road',
       'Urban_or_Rural_Area_Rural', 'Urban_or_Rural_Area_Unallocated',
       'Urban_or_Rural_Area_Urban', 'Vehicle_Type_Agricultural vehicle',
       'Vehicle_Type_Bus or coach (17 or more pass seats)', 'Vehicle_Type_Car',
       'Vehicle_Type_Data missing or out of range',
       'Vehicle_Type_Goods 7.5 tonnes mgw and over',
       'Vehicle_Type_Goods over 3.5t. and under 7.5t',
       'Vehicle_Type_Minibus (8 - 16 passenger seats)',
       'Vehicle_Type_Motorcycle 125cc and under',
       'Vehicle_Type_Motorcycle 50cc and under',
       'Vehicle_Type_Motorcycle over 125cc and up to 500cc',
       'Vehicle_Type_Motorcycle over 500cc', 'Vehicle_Type_Other vehicle',
       'Vehicle_Type_Pedal cycle', 'Vehicle_Type_Ridden horse',
       'Vehicle_Type_Taxi/Private hire car',
       'Vehicle_Type_Van / Goods 3.5 tonnes mgw or under',
       'Weather_Conditions_Fine + high winds',
       'Weather_Conditions_Fine no high winds',
       'Weather_Conditions_Fog or mist', 'Weather_Conditions_Other',
       'Weather_Conditions_Raining + high winds',
       'Weather_Conditions_Raining no high winds',
       'Weather_Conditions_Snowing + high winds',
       'Weather_Conditions_Snowing no high winds'],
      dtype='object')
Missing columns: ['Light_Conditions', 'Road_Surface_Conditions', 'Road_Type', 'Urban_or_Rural_Area', 'Vehicle_Type', 'Weather_Conditions']
Index(['Accident_Severity', 'Number_of_Casualties', 'Number_of_Vehicles',
       'Light_Conditions_Darkness - lighting unknown',
       'Light_Conditions_Darkness - lights lit',
       'Light_Conditions_Darkness - lights unlit',
       'Light_Conditions_Darkness - no lighting', 'Light_Conditions_Daylight',
       'Road_Surface_Conditions_Dry',
       'Road_Surface_Conditions_Flood over 3cm. deep',
       'Road_Surface_Conditions_Frost or ice', 'Road_Surface_Conditions_Snow',
       'Road_Surface_Conditions_Wet or damp', 'Road_Type_Dual carriageway',
       'Road_Type_One way street', 'Road_Type_Roundabout',
       'Road_Type_Single carriageway', 'Road_Type_Slip road',
       'Urban_or_Rural_Area_Rural', 'Urban_or_Rural_Area_Unallocated',
       'Urban_or_Rural_Area_Urban', 'Vehicle_Type_Agricultural vehicle',
       'Vehicle_Type_Bus or coach (17 or more pass seats)', 'Vehicle_Type_Car',
       'Vehicle_Type_Data missing or out of range',
       'Vehicle_Type_Goods 7.5 tonnes mgw and over',
       'Vehicle_Type_Goods over 3.5t. and under 7.5t',
       'Vehicle_Type_Minibus (8 - 16 passenger seats)',
       'Vehicle_Type_Motorcycle 125cc and under',
       'Vehicle_Type_Motorcycle 50cc and under',
       'Vehicle_Type_Motorcycle over 125cc and up to 500cc',
       'Vehicle_Type_Motorcycle over 500cc', 'Vehicle_Type_Other vehicle',
       'Vehicle_Type_Pedal cycle', 'Vehicle_Type_Ridden horse',
       'Vehicle_Type_Taxi/Private hire car',
       'Vehicle_Type_Van / Goods 3.5 tonnes mgw or under',
       'Weather_Conditions_Fine + high winds',
       'Weather_Conditions_Fine no high winds',
       'Weather_Conditions_Fog or mist', 'Weather_Conditions_Other',
       'Weather_Conditions_Raining + high winds',
       'Weather_Conditions_Raining no high winds',
       'Weather_Conditions_Snowing + high winds',
       'Weather_Conditions_Snowing no high winds'],
      dtype='object')
In [76]:
df
Out[76]:
Accident_Severity Number_of_Casualties Number_of_Vehicles Light_Conditions_Darkness - lighting unknown Light_Conditions_Darkness - lights lit Light_Conditions_Darkness - lights unlit Light_Conditions_Darkness - no lighting Light_Conditions_Daylight Road_Surface_Conditions_Dry Road_Surface_Conditions_Flood over 3cm. deep ... Vehicle_Type_Taxi/Private hire car Vehicle_Type_Van / Goods 3.5 tonnes mgw or under Weather_Conditions_Fine + high winds Weather_Conditions_Fine no high winds Weather_Conditions_Fog or mist Weather_Conditions_Other Weather_Conditions_Raining + high winds Weather_Conditions_Raining no high winds Weather_Conditions_Snowing + high winds Weather_Conditions_Snowing no high winds
0 NaN 1 2 0 1 0 0 0 1 0 ... 0 0 0 1 0 0 0 0 0 0
1 NaN 1 2 0 0 0 0 1 0 0 ... 0 0 0 0 0 0 0 1 0 0
2 NaN 1 3 1 0 0 0 0 1 0 ... 1 0 0 1 0 0 0 0 0 0
3 NaN 1 4 0 0 0 0 1 1 0 ... 0 0 0 1 0 0 0 0 0 0
4 NaN 1 2 0 1 0 0 0 1 0 ... 0 0 0 1 0 0 0 0 0 0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
660674 NaN 2 1 0 0 0 0 1 1 0 ... 0 0 0 1 0 0 0 0 0 0
660675 NaN 1 1 0 0 0 1 0 0 0 ... 0 0 0 1 0 0 0 0 0 0
660676 NaN 1 3 0 0 0 0 1 0 0 ... 0 0 0 1 0 0 0 0 0 0
660677 NaN 1 2 0 0 0 1 0 0 0 ... 0 0 0 1 0 0 0 0 0 0
660678 NaN 1 1 0 0 0 0 1 0 0 ... 0 0 0 0 0 0 0 0 0 1

660660 rows × 45 columns

In [89]:
from sklearn.model_selection import train_test_split
X = df.drop('Accident_Severity', axis=1)
y = df['Accident_Severity']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, stratify = y, random_state=42)
In [90]:
from sklearn.linear_model import LogisticRegression
In [91]:
model = LogisticRegression(random_state=42)
In [92]:
model.fit(X_train,y_train)
Out[92]:
LogisticRegression(random_state=42)
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LogisticRegression(random_state=42)
In [93]:
y_pred = model.predict(X_test)
In [94]:
from sklearn.metrics import accuracy_score, classification_report, confusion_matrix
In [95]:
accuracy = accuracy_score(y_test, y_pred)
conf_matrix = confusion_matrix(y_test, y_pred)
classification_rep = classification_report(y_test, y_pred)
print(f'Accuracy: {accuracy:.2f}')
print(f'Confusion Matrix:\n{conf_matrix}')
print(f'Classification Report:\n{classification_rep}')

Accuracy: 0.85
Confusion Matrix:
[[169083     43      9]
 [ 26430     28      7]
 [  2580     11      7]]
Classification Report:
              precision    recall  f1-score   support

           0       0.85      1.00      0.92    169135
           1       0.34      0.00      0.00     26465
           2       0.30      0.00      0.01      2598

    accuracy                           0.85    198198
   macro avg       0.50      0.33      0.31    198198
weighted avg       0.78      0.85      0.79    198198
In [75]:
from sklearn.ensemble import RandomForestClassifier

# Initialize Random Forest model
forest = RandomForestClassifier(n_estimators=100, max_depth=5, random_state=42)
In [76]:
# Fit model
forest.fit(X_train, y_train)
Out[76]:
RandomForestClassifier(max_depth=5, random_state=42)
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RandomForestClassifier(max_depth=5, random_state=42)
In [77]:
# Predict and calculate accuracy
y_pred = forest.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print(f"Random Forest Accuracy: {accuracy}")

Random Forest Accuracy: 0.8533663306390579
In [78]:
accuracy = accuracy_score(y_test, y_pred)
conf_matrix = confusion_matrix(y_test, y_pred)
classification_rep = classification_report(y_test, y_pred)
print(f'Accuracy: {accuracy:.2f}')
print(f'Confusion Matrix:\n{conf_matrix}')
print(f'Classification Report:\n{classification_rep}')

Accuracy: 0.85
Confusion Matrix:
[[112757      0      0]
 [ 17643      0      0]
 [  1732      0      0]]
Classification Report:
              precision    recall  f1-score   support

           0       0.85      1.00      0.92    112757
           1       0.00      0.00      0.00     17643
           2       0.00      0.00      0.00      1732

    accuracy                           0.85    132132
   macro avg       0.28      0.33      0.31    132132
weighted avg       0.73      0.85      0.79    132132


D:\Users\tamil\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
D:\Users\tamil\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
D:\Users\tamil\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
In [79]:
from sklearn.tree import DecisionTreeClassifier

# Initialize Decision Tree model
tree = DecisionTreeClassifier(max_depth=5)
In [80]:
# Fit model
tree.fit(X_train, y_train)

# Predict and calculate accuracy
y_pred = tree.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print(f"Decision Tree Accuracy: {accuracy}")

Decision Tree Accuracy: 0.853328489692126
In [81]:
accuracy = accuracy_score(y_test, y_pred)
conf_matrix = confusion_matrix(y_test, y_pred)
classification_rep = classification_report(y_test, y_pred)
print(f'Accuracy: {accuracy:.2f}')
print(f'Confusion Matrix:\n{conf_matrix}')
print(f'Classification Report:\n{classification_rep}')

Accuracy: 0.85
Confusion Matrix:
[[112744     13      0]
 [ 17635      8      0]
 [  1728      4      0]]
Classification Report:
              precision    recall  f1-score   support

           0       0.85      1.00      0.92    112757
           1       0.32      0.00      0.00     17643
           2       0.00      0.00      0.00      1732

    accuracy                           0.85    132132
   macro avg       0.39      0.33      0.31    132132
weighted avg       0.77      0.85      0.79    132132


D:\Users\tamil\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
D:\Users\tamil\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
D:\Users\tamil\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
In [ ]:
In [76]:
from sklearn.neighbors import KNeighborsClassifier

# Initialize the model
model = KNeighborsClassifier(n_neighbors=5)  # k=5
In [77]:
# Train the model
model.fit(X_train, y_train)
Out[77]:
KNeighborsClassifier()
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KNeighborsClassifier()
In [78]:
# Make predictions
y_pred = model.predict(X_test)

# Evaluate the model
accuracy = accuracy_score(y_test, y_pred)
print(f"KNN Accuracy: {accuracy}")
print("Confusion Matrix:\n", confusion_matrix(y_test, y_pred))
print("Classification Report:\n", classification_report(y_test, y_pred))

KNN Accuracy: 0.8425589561953198
Confusion Matrix:
 [[110738   2014      5]
 [ 17050    590      3]
 [  1633     98      1]]
Classification Report:
               precision    recall  f1-score   support

           0       0.86      0.98      0.91    112757
           1       0.22      0.03      0.06     17643
           2       0.11      0.00      0.00      1732

    accuracy                           0.84    132132
   macro avg       0.40      0.34      0.32    132132
weighted avg       0.76      0.84      0.79    132132
In [80]:
from sklearn.ensemble import GradientBoostingClassifier

# Initialize the model
model = GradientBoostingClassifier(n_estimators=100, learning_rate=0.1, max_depth=3)
In [81]:
# Train the model
model.fit(X_train, y_train)
Out[81]:
GradientBoostingClassifier()
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GradientBoostingClassifier()
In [82]:
# Make predictions
y_pred = model.predict(X_test)
In [83]:
# Evaluate the model
accuracy = accuracy_score(y_test, y_pred)
print(f"Gradient Boosting Accuracy: {accuracy}")
print("Confusion Matrix:\n", confusion_matrix(y_test, y_pred))
print("Classification Report:\n", classification_report(y_test, y_pred))

Gradient Boosting Accuracy: 0.8533057851239669
Confusion Matrix:
 [[112729     27      1]
 [ 17623     20      0]
 [  1716     16      0]]
Classification Report:
               precision    recall  f1-score   support

           0       0.85      1.00      0.92    112757
           1       0.32      0.00      0.00     17643
           2       0.00      0.00      0.00      1732

    accuracy                           0.85    132132
   macro avg       0.39      0.33      0.31    132132
weighted avg       0.77      0.85      0.79    132132
In [84]:
from sklearn.neural_network import MLPClassifier

# Initialize the model
model = MLPClassifier(hidden_layer_sizes=(50, 30), max_iter=500, random_state=42)
In [85]:
# Train the model
model.fit(X_train, y_train)
Out[85]:
MLPClassifier(hidden_layer_sizes=(50, 30), max_iter=500, random_state=42)
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MLPClassifier(hidden_layer_sizes=(50, 30), max_iter=500, random_state=42)
In [86]:
# Make predictions
y_pred = model.predict(X_test)
In [87]:
# Evaluate the model
accuracy = accuracy_score(y_test, y_pred)
print(f"Neural Network (MLP) Accuracy: {accuracy}")
print("Confusion Matrix:\n", confusion_matrix(y_test, y_pred))
print("Classification Report:\n", classification_report(y_test, y_pred))

Neural Network (MLP) Accuracy: 0.853245239608876
Confusion Matrix:
 [[112720     37      0]
 [ 17621     21      1]
 [  1719     13      0]]
Classification Report:
               precision    recall  f1-score   support

           0       0.85      1.00      0.92    112757
           1       0.30      0.00      0.00     17643
           2       0.00      0.00      0.00      1732

    accuracy                           0.85    132132
   macro avg       0.38      0.33      0.31    132132
weighted avg       0.77      0.85      0.79    132132
In [86]:
!pip install -U scikit-learn
!pip install -U imbalanced-learn

Requirement already satisfied: scikit-learn in d:\users\tamil\anaconda3\lib\site-packages (1.5.2)
Requirement already satisfied: numpy>=1.19.5 in d:\users\tamil\anaconda3\lib\site-packages (from scikit-learn) (1.24.3)
Requirement already satisfied: scipy>=1.6.0 in d:\users\tamil\anaconda3\lib\site-packages (from scikit-learn) (1.12.0)
Requirement already satisfied: joblib>=1.2.0 in d:\users\tamil\anaconda3\lib\site-packages (from scikit-learn) (1.2.0)
Requirement already satisfied: threadpoolctl>=3.1.0 in d:\users\tamil\anaconda3\lib\site-packages (from scikit-learn) (3.5.0)

[notice] A new release of pip is available: 24.2 -> 24.3.1
[notice] To update, run: python.exe -m pip install --upgrade pip

Requirement already satisfied: imbalanced-learn in d:\users\tamil\anaconda3\lib\site-packages (0.12.4)
Requirement already satisfied: numpy>=1.17.3 in d:\users\tamil\anaconda3\lib\site-packages (from imbalanced-learn) (1.24.3)
Requirement already satisfied: scipy>=1.5.0 in d:\users\tamil\anaconda3\lib\site-packages (from imbalanced-learn) (1.12.0)
Requirement already satisfied: scikit-learn>=1.0.2 in d:\users\tamil\anaconda3\lib\site-packages (from imbalanced-learn) (1.5.2)
Requirement already satisfied: joblib>=1.1.1 in d:\users\tamil\anaconda3\lib\site-packages (from imbalanced-learn) (1.2.0)
Requirement already satisfied: threadpoolctl>=2.0.0 in d:\users\tamil\anaconda3\lib\site-packages (from imbalanced-learn) (3.5.0)

[notice] A new release of pip is available: 24.2 -> 24.3.1
[notice] To update, run: python.exe -m pip install --upgrade pip
In [82]:
import numpy as np
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout
from tensorflow.keras.utils import to_categorical
from sklearn.metrics import classification_report, confusion_matrix
In [83]:
# One-hot encode labels if needed (useful for multi-class classification)
y_train_oh = to_categorical(y_train)
y_test_oh = to_categorical(y_test)
In [84]:
# Define the model structure
model = Sequential()
model.add(Dense(128, input_shape=(X_train.shape[1],), activation='relu'))
model.add(Dropout(0.3))  # Dropout layer to reduce overfitting
model.add(Dense(64, activation='relu'))
model.add(Dropout(0.3))
model.add(Dense(32, activation='relu'))
model.add(Dense(y_train_oh.shape[1], activation='softmax'))  # Output layer with softmax for multi-class classification

D:\Users\tamil\anaconda3\Lib\site-packages\keras\src\layers\core\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
  super().__init__(activity_regularizer=activity_regularizer, **kwargs)
In [85]:
# Compile the model
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
In [86]:
# Train the model
model.fit(X_train, y_train_oh, epochs=50, batch_size=32, validation_split=0.2, verbose=1)

Epoch 1/50
13214/13214 ━━━━━━━━━━━━━━━━━━━━ 33s 2ms/step - accuracy: 0.8520 - loss: 0.4557 - val_accuracy: 0.8536 - val_loss: 0.4411
Epoch 2/50
13214/13214 ━━━━━━━━━━━━━━━━━━━━ 31s 2ms/step - accuracy: 0.8524 - loss: 0.4448 - val_accuracy: 0.8536 - val_loss: 0.4407
Epoch 3/50
13214/13214 ━━━━━━━━━━━━━━━━━━━━ 33s 2ms/step - accuracy: 0.8532 - loss: 0.4418 - val_accuracy: 0.8536 - val_loss: 0.4420
Epoch 4/50
13214/13214 ━━━━━━━━━━━━━━━━━━━━ 33s 2ms/step - accuracy: 0.8536 - loss: 0.4417 - val_accuracy: 0.8536 - val_loss: 0.4406
Epoch 5/50
13214/13214 ━━━━━━━━━━━━━━━━━━━━ 31s 2ms/step - accuracy: 0.8538 - loss: 0.4411 - val_accuracy: 0.8535 - val_loss: 0.4409
Epoch 6/50
13214/13214 ━━━━━━━━━━━━━━━━━━━━ 31s 2ms/step - accuracy: 0.8524 - loss: 0.4439 - val_accuracy: 0.8536 - val_loss: 0.4408
Epoch 7/50
13214/13214 ━━━━━━━━━━━━━━━━━━━━ 33s 2ms/step - accuracy: 0.8533 - loss: 0.4430 - val_accuracy: 0.8536 - val_loss: 0.4412
Epoch 8/50
13214/13214 ━━━━━━━━━━━━━━━━━━━━ 32s 2ms/step - accuracy: 0.8528 - loss: 0.4434 - val_accuracy: 0.8536 - val_loss: 0.4406
Epoch 9/50
13214/13214 ━━━━━━━━━━━━━━━━━━━━ 31s 2ms/step - accuracy: 0.8539 - loss: 0.4406 - val_accuracy: 0.8536 - val_loss: 0.4403
Epoch 10/50
13214/13214 ━━━━━━━━━━━━━━━━━━━━ 31s 2ms/step - accuracy: 0.8530 - loss: 0.4421 - val_accuracy: 0.8536 - val_loss: 0.4409
Epoch 11/50
13214/13214 ━━━━━━━━━━━━━━━━━━━━ 31s 2ms/step - accuracy: 0.8540 - loss: 0.4402 - val_accuracy: 0.8536 - val_loss: 0.4407
Epoch 12/50
13214/13214 ━━━━━━━━━━━━━━━━━━━━ 32s 2ms/step - accuracy: 0.8527 - loss: 0.4426 - val_accuracy: 0.8535 - val_loss: 0.4406
Epoch 13/50
13214/13214 ━━━━━━━━━━━━━━━━━━━━ 31s 2ms/step - accuracy: 0.8530 - loss: 0.4413 - val_accuracy: 0.8535 - val_loss: 0.4410
Epoch 14/50
13214/13214 ━━━━━━━━━━━━━━━━━━━━ 31s 2ms/step - accuracy: 0.8528 - loss: 0.4423 - val_accuracy: 0.8536 - val_loss: 0.4405
Epoch 15/50
13214/13214 ━━━━━━━━━━━━━━━━━━━━ 32s 2ms/step - accuracy: 0.8529 - loss: 0.4415 - val_accuracy: 0.8536 - val_loss: 0.4412
Epoch 16/50
13214/13214 ━━━━━━━━━━━━━━━━━━━━ 31s 2ms/step - accuracy: 0.8534 - loss: 0.4413 - val_accuracy: 0.8535 - val_loss: 0.4404
Epoch 17/50
13214/13214 ━━━━━━━━━━━━━━━━━━━━ 32s 2ms/step - accuracy: 0.8532 - loss: 0.4419 - val_accuracy: 0.8536 - val_loss: 0.4404
Epoch 18/50
13214/13214 ━━━━━━━━━━━━━━━━━━━━ 31s 2ms/step - accuracy: 0.8519 - loss: 0.4438 - val_accuracy: 0.8536 - val_loss: 0.4419
Epoch 19/50
13214/13214 ━━━━━━━━━━━━━━━━━━━━ 31s 2ms/step - accuracy: 0.8535 - loss: 0.4411 - val_accuracy: 0.8536 - val_loss: 0.4408
Epoch 20/50
13214/13214 ━━━━━━━━━━━━━━━━━━━━ 31s 2ms/step - accuracy: 0.8537 - loss: 0.4410 - val_accuracy: 0.8535 - val_loss: 0.4410
Epoch 21/50
13214/13214 ━━━━━━━━━━━━━━━━━━━━ 31s 2ms/step - accuracy: 0.8530 - loss: 0.4432 - val_accuracy: 0.8536 - val_loss: 0.4407
Epoch 22/50
13214/13214 ━━━━━━━━━━━━━━━━━━━━ 32s 2ms/step - accuracy: 0.8545 - loss: 0.4388 - val_accuracy: 0.8536 - val_loss: 0.4413
Epoch 23/50
13214/13214 ━━━━━━━━━━━━━━━━━━━━ 101s 8ms/step - accuracy: 0.8541 - loss: 0.4389 - val_accuracy: 0.8535 - val_loss: 0.4404
Epoch 24/50
13214/13214 ━━━━━━━━━━━━━━━━━━━━ 31s 2ms/step - accuracy: 0.8535 - loss: 0.4410 - val_accuracy: 0.8536 - val_loss: 0.4407
Epoch 25/50
13214/13214 ━━━━━━━━━━━━━━━━━━━━ 31s 2ms/step - accuracy: 0.8537 - loss: 0.4406 - val_accuracy: 0.8535 - val_loss: 0.4410
Epoch 26/50
13214/13214 ━━━━━━━━━━━━━━━━━━━━ 32s 2ms/step - accuracy: 0.8537 - loss: 0.4406 - val_accuracy: 0.8534 - val_loss: 0.4415
Epoch 27/50
13214/13214 ━━━━━━━━━━━━━━━━━━━━ 31s 2ms/step - accuracy: 0.8522 - loss: 0.4439 - val_accuracy: 0.8536 - val_loss: 0.4414
Epoch 28/50
13214/13214 ━━━━━━━━━━━━━━━━━━━━ 32s 2ms/step - accuracy: 0.8535 - loss: 0.4415 - val_accuracy: 0.8535 - val_loss: 0.4408
Epoch 29/50
13214/13214 ━━━━━━━━━━━━━━━━━━━━ 31s 2ms/step - accuracy: 0.8535 - loss: 0.4414 - val_accuracy: 0.8535 - val_loss: 0.4412
Epoch 30/50
13214/13214 ━━━━━━━━━━━━━━━━━━━━ 32s 2ms/step - accuracy: 0.8532 - loss: 0.4416 - val_accuracy: 0.8536 - val_loss: 0.4414
Epoch 31/50
13214/13214 ━━━━━━━━━━━━━━━━━━━━ 32s 2ms/step - accuracy: 0.8526 - loss: 0.4431 - val_accuracy: 0.8536 - val_loss: 0.4414
Epoch 32/50
13214/13214 ━━━━━━━━━━━━━━━━━━━━ 33s 2ms/step - accuracy: 0.8533 - loss: 0.4415 - val_accuracy: 0.8536 - val_loss: 0.4415
Epoch 33/50
13214/13214 ━━━━━━━━━━━━━━━━━━━━ 32s 2ms/step - accuracy: 0.8530 - loss: 0.4415 - val_accuracy: 0.8536 - val_loss: 0.4412
Epoch 34/50
13214/13214 ━━━━━━━━━━━━━━━━━━━━ 31s 2ms/step - accuracy: 0.8536 - loss: 0.4415 - val_accuracy: 0.8536 - val_loss: 0.4414
Epoch 35/50
13214/13214 ━━━━━━━━━━━━━━━━━━━━ 32s 2ms/step - accuracy: 0.8543 - loss: 0.4406 - val_accuracy: 0.8536 - val_loss: 0.4406
Epoch 36/50
13214/13214 ━━━━━━━━━━━━━━━━━━━━ 33s 2ms/step - accuracy: 0.8533 - loss: 0.4411 - val_accuracy: 0.8536 - val_loss: 0.4407
Epoch 37/50
13214/13214 ━━━━━━━━━━━━━━━━━━━━ 31s 2ms/step - accuracy: 0.8529 - loss: 0.4428 - val_accuracy: 0.8535 - val_loss: 0.4405
Epoch 38/50
13214/13214 ━━━━━━━━━━━━━━━━━━━━ 31s 2ms/step - accuracy: 0.8537 - loss: 0.4395 - val_accuracy: 0.8536 - val_loss: 0.4416
Epoch 39/50
13214/13214 ━━━━━━━━━━━━━━━━━━━━ 31s 2ms/step - accuracy: 0.8524 - loss: 0.4430 - val_accuracy: 0.8536 - val_loss: 0.4411
Epoch 40/50
13214/13214 ━━━━━━━━━━━━━━━━━━━━ 31s 2ms/step - accuracy: 0.8532 - loss: 0.4424 - val_accuracy: 0.8536 - val_loss: 0.4416
Epoch 41/50
13214/13214 ━━━━━━━━━━━━━━━━━━━━ 33s 2ms/step - accuracy: 0.8534 - loss: 0.4415 - val_accuracy: 0.8536 - val_loss: 0.4406
Epoch 42/50
13214/13214 ━━━━━━━━━━━━━━━━━━━━ 33s 2ms/step - accuracy: 0.8532 - loss: 0.4418 - val_accuracy: 0.8535 - val_loss: 0.4411
Epoch 43/50
13214/13214 ━━━━━━━━━━━━━━━━━━━━ 33s 2ms/step - accuracy: 0.8530 - loss: 0.4420 - val_accuracy: 0.8536 - val_loss: 0.4410
Epoch 44/50
13214/13214 ━━━━━━━━━━━━━━━━━━━━ 34s 3ms/step - accuracy: 0.8528 - loss: 0.4431 - val_accuracy: 0.8536 - val_loss: 0.4410
Epoch 45/50
13214/13214 ━━━━━━━━━━━━━━━━━━━━ 34s 3ms/step - accuracy: 0.8522 - loss: 0.4431 - val_accuracy: 0.8535 - val_loss: 0.4413
Epoch 46/50
13214/13214 ━━━━━━━━━━━━━━━━━━━━ 31s 2ms/step - accuracy: 0.8529 - loss: 0.4425 - val_accuracy: 0.8535 - val_loss: 0.4413
Epoch 47/50
13214/13214 ━━━━━━━━━━━━━━━━━━━━ 31s 2ms/step - accuracy: 0.8532 - loss: 0.4421 - val_accuracy: 0.8536 - val_loss: 0.4413
Epoch 48/50
13214/13214 ━━━━━━━━━━━━━━━━━━━━ 31s 2ms/step - accuracy: 0.8529 - loss: 0.4421 - val_accuracy: 0.8535 - val_loss: 0.4414
Epoch 49/50
13214/13214 ━━━━━━━━━━━━━━━━━━━━ 31s 2ms/step - accuracy: 0.8536 - loss: 0.4401 - val_accuracy: 0.8536 - val_loss: 0.4415
Epoch 50/50
13214/13214 ━━━━━━━━━━━━━━━━━━━━ 30s 2ms/step - accuracy: 0.8530 - loss: 0.4422 - val_accuracy: 0.8536 - val_loss: 0.4408
Out[86]:
<keras.src.callbacks.history.History at 0x188cf23ed10>
In [87]:
# Evaluate on the test data
y_pred = model.predict(X_test)
y_pred_classes = np.argmax(y_pred, axis=1)  # Convert predictions to class labels

4130/4130 ━━━━━━━━━━━━━━━━━━━━ 7s 2ms/step
In [88]:
# Display metrics
print("Confusion Matrix:\n", confusion_matrix(y_test, y_pred_classes))
print("Classification Report:\n", classification_report(y_test, y_pred_classes))

Confusion Matrix:
 [[112750      6      1]
 [ 17637      6      0]
 [  1726      3      3]]
Classification Report:
               precision    recall  f1-score   support

           0       0.85      1.00      0.92    112757
           1       0.40      0.00      0.00     17643
           2       0.75      0.00      0.00      1732

    accuracy                           0.85    132132
   macro avg       0.67      0.33      0.31    132132
weighted avg       0.79      0.85      0.79    132132
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